Stochastic Analysis of Multi-Reaction Model for Non-Linear Thermal History

ANTHONY, D. B. – HOWARD, J. B. 1976. Coal devolatilization and hydrogastification. In Journal of American Chemical Society, vol. 22, no. 4, pp. 625–656.10.1002/aic.690220403Search in Google Scholar

BURNHAM, A. K. 2017. Introduction to chemical kinetics. In Global Chemical Kinetics of Fossil Fuels, pp. 25–74.10.1007/978-3-319-49634-4_2Search in Google Scholar

BURNHAM, A. K. 2014. Obtaining reliable phenomenological chemical kinetic models for real-world applications. In Thermochimica Acta, vol. 597, pp. 35–40.10.1016/j.tca.2014.10.006Search in Google Scholar

CAI, J. M. – Li, T. – LIU, R. A. 2007. A critical study of the Miura-Maki integral method for the estimation of the kinetic parameters of the distributed activation energy model. In Journal of Bioresource Technology, vol. 102, pp. 3894–3899.10.1016/j.biortech.2010.11.110Search in Google Scholar

DHAUNDIYAL, A – SINGH, S. B. – ATSU, D. – DHAUNDIYAL, R. 2019. Application of Monte Carlo simulation for energy modelling. In Journal of American Chemical Society (Omega), vol. 4, no. 3, pp. 4984–4990.10.1021/acsomega.8b03442Search in Google Scholar

DHAUNDIYAL, A. – B. SINGH, S. 2018a. Mathematical insight to non-isothermal pyrolysis of pine needles for different probability distribution functions. In Biofuels, vol. 9, no. 5, pp. 647–658.10.1080/17597269.2017.1329495Search in Google Scholar

DHAUNDIYAL, A. – SINGH, S. B – HAMON, M. 2018b. Study of distributed activation energy model using bivariate distribution function, f (E₁, E₂). In Thermal Science and Engineering Progress, vol. 5, pp. 388–404.10.1016/j.tsep.2018.01.009Search in Google Scholar

DHAUNDIYAL, A. – SINGH, S. B. 2017a. Asymptotic approximations to the isothermal pyrolysis of deodara leaves using gamma distribution. In Journal of Universitas Scientiarum, vol. 22, no. 3, pp. 263–284.10.11144/Javeriana.SC22-3.aattSearch in Google Scholar

DHAUNDIYAL, A. – SINGH, S. B. 2017b. Approximations to the non-isothermal distributed activation energy model for biomass pyrolysis using the Rayleigh distribution. In Acta Technologica Agriculturae, vol. 20, no. 3, pp.78–84.10.1515/ata-2017-0016Search in Google Scholar

DHAUNDIYAL, A. – TEWARI, P. 2017. Kinetic parameters for the thermal decomposition of forest waste using distributed activation energy model (DAEM). In Environmental and Climate Technologies, vol. 19, no. 1, pp. 15–32.10.1515/rtuect-2017-0002Search in Google Scholar

LI, Z. – LIU, C. – CHEN, Z. – QUAIN, J. – ZHAO, W. – ZHU, Q. 2009. Analysis of coals and biomass pyrolysis using the distributed activation energy model. In Bioresource Technology, vol. 100, no. 2, pp. 948–952.10.1016/j.biortech.2008.07.032Search in Google Scholar

MIURA, K. 1995. A new and simple method to estimate f(E) and ko(E) in the distributed activation energy model from three sets of experimental data. In Journal of Energy and Fuels, vol. 9, no. 2, pp. 302–307.10.1021/ef00050a014Search in Google Scholar

YAN, J. H. – ZHU, H. M. – JIANG, X. G. – CHI, Y. – CEN, K. F. 2009. Analysis of volatile species kinetics during typical medical waste materials pyrolysis using a distributed activation energy model. In Journal of Hazardous Materials, vol. 162, no. 2–3, pp. 646–651.10.1016/j.jhazmat.2008.05.077Search in Google Scholar